About this Course

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Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Approx. 12 hours to complete

English

Subtitles: English

What you will learn

  • Use TensorFlow Serving to do inference over the web

  • Navigate TensorFlow Hub, a repository of models that you can use for transfer learning

  • Evaluate how your models work and share model metadata using TensorBoard

  • Explore federated learning and how to retrain deployed models while maintaining data privacy

Skills you will gain

TensorFlow ServingMachine Learningfederated learningTensorFlow HubTensorBoard

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Approx. 12 hours to complete

English

Subtitles: English

Instructor

Offered by

deeplearning.ai logo

deeplearning.ai

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

TensorFlow Extended

1 hour to complete
12 videos (Total 21 min), 5 readings, 1 quiz
12 videos
Introduction24s
Serving3m
Installing TF Serving1m
TensorFlow Serving summary30s
Setup for serving2m
Serving1m
Predictions41s
Passing data to serving1m
Getting the predictions back1m
Running the colab2m
Complex model2m
5 readings
Downloading the Coding Examples and Exercises10m
Installation link10m
TF server running in colab10m
Serving with Fashion MNIST10m
Ungraded Exercise - Serving with MNIST10m
1 practice exercise
Week 1 Quiz
Week
2

Week 2

5 hours to complete

Sharing pre-trained models with TensorFlow Hub

5 hours to complete
11 videos (Total 20 min), 7 readings, 2 quizzes
11 videos
Introduction to TF Hub2m
Transfer learning1m
Inference1m
Module storage2m
Text based models1m
Word embeddings1m
Experimenting with embeddings1m
Colab1m
Classify cats and dogs1m
Transfer learning1m
7 readings
Tensorflow Hub link10m
Link to saved models10m
Colab10m
Pre-trained Word Embeddings10m
Text Classification Colab10m
MobileNet model details10m
Colab10m
1 practice exercise
Week 2 Quiz
Week
3

Week 3

5 hours to complete

Tensorboard: tools for model training

5 hours to complete
10 videos (Total 16 min), 2 readings, 2 quizzes
10 videos
Tensorboard scalars1m
Callbacks42s
Histograms59s
Publishing model details1m
Local tensorboard2m
Looking at graphics in a dataset2m
More than one image56s
Confusion matrix2m
Multiple callbacks1m
2 readings
tensorboard.dev10m
Colab10m
1 practice exercise
Week 3 Quiz4m
Week
4

Week 4

1 hour to complete

Federated Learning

1 hour to complete
9 videos (Total 22 min), 1 reading, 1 quiz
9 videos
Training on mobile devices2m
Data at the edge2m
How it works2m
Maintaining user privacy3m
Masking2m
APIs for Federated Learning2m
Example of federated learning2m
Outro59s
1 reading
Colab10m
1 practice exercise
Week 4 Quiz30m

About the TensorFlow: Data and Deployment Specialization

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment....
TensorFlow: Data and Deployment

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.